The use of satellite-based and aerial-based imagery is popular among government and commercial entities. One of the challenges in obtaining high quality images of the earth is the presence of the atmosphere between the surface of the earth and the satellite collecting the image. This atmosphere has water vapor and aerosols therein that can cause the absorption and scattering of light. This scattering can redirect light in an undesirable fashion. This can include scattering desirable light away from the satellite's imaging system as well as scattering undesirable light toward the imaging system.
If the atmospheric conditions are sufficiently understood, it might be theoretically possible to convert the captured image of the earth's surface (a measurement of radiance received at the satellite) to an image of surface reflectance. Several techniques exist for making such atmospheric corrections or compensations to images—Quick Atmospheric Correction (QUAC) and Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH) are some of the most common as they are both part of the ENVI software suite available from Exelis Visual Information Solutions, Inc. FLAASH is highly accurate, but requires the knowledge of the atmospheric components and their manual input and is thus fairly slow and often it is not possible to obtain reliable atmospheric information to perform the correction. QUAC is fully automated and thus much faster, but its accuracy is much lower than FLAASH.
QUAC is a visible-near infrared through shortwave infrared (VNIR-SWIR) atmospheric compensation method for multispectral and hyperspectral imagery. As used herein, “multispectral” and “hyperspectral” each refer to a plurality of discrete spectral bands (e.g., a blue band in the 442-515 nanometer range, a green band in the 506-586 nanometer range, a yellow band in the 584-632 nanometer range, a red band in the 624-694 nanometer range, and other discrete color bands) with multispectral referring to systems with a relatively small number of discrete bands (e.g., 7 bands) and hyperspectral referring to systems with a large number of discrete bands (e.g., 110 bands). “Panchromatic” is a term that refers to a single band with a broad wavelength range and may also be referred to as black-and-white (e.g., 405-1053 nanometers, 397-905 nanometers, 447-808 nanometers or any other range that covers a broad spectrum. Often, panchromatic bands cover a substantial majority of the visible and/or near-infrared light spectrum, but this is not necessarily the case.
Unlike other first-principles atmospheric correction methods, QUAC determines atmospheric compensation parameters directly from the information contained within the scene (observed pixel spectra), without ancillary information. QUAC performs a more approximate atmospheric correction than FLAASH or other physics-based first-principles methods, generally producing reflectance spectra within approximately +/−15% of the physics-based approaches. QUAC is based on the empirical finding that the average reflectance of a collection of diverse material spectra, such as the endmember spectra in a scene, is essentially scene-independent. All of this means significantly faster computational speed compared to the first-principles methods. FLAASH is a first-principles atmospheric correction tool that corrects wavelengths in the visible through near-infrared and shortwave infrared regions, up to 3000 nm. Unlike many other atmospheric correction programs that interpolate radiation transfer properties from a pre-calculated database of modeling results, FLAASH incorporates the MODTRAN4 radiation transfer code. Again, as stated above, FLAASH is highly accurate, but requires manual input and is thus fairly slow. QUAC is fully automated and thus much faster, but its accuracy is much lower than FLAASH. And, each of QUAC and FLAASH require multispectral data, and cannot operate with panchromatic data alone.
Highly accurate classification of landcover types and states is essential to extracting useful information, insight and prediction for a wide variety of applications. In many cases, this classification of type and state is dependent on multi-temporal observations. In all cases, there are a number of confounding factors to deal with including opaque clouds, cirrus clouds, aerosols, water vapor, ice, snow, shadows, bidirectional reflectance distribution factor (BRDF) effects and transient coverings like water, dust, snow, ice and mobile objects. Pseudo invariant objects (PIOs) are often used for on-orbit calibration of relatively stable sensors because the PIOs are in useful states often enough. But there are not enough truly stable PIOs in the world with required spatial density to deal with the highly variable confounding factors of images.
Prior art makes simplifying assumptions as to presence and stability of calibrating materials, and uniformity of atmospheric effects that introduce significant errors across images. We have determined that ignoring the dynamic phenological variations and atmospheric element gradients within a scene can create classification errors of 45% or more. Multi-temporal anomaly detection suffers accordingly.